Authors:Seketoulie Keretsu, Rosy SarmahPages: 313 - 328Abstract: Due to the advancement in Proteomic technologies, bulk data of protein-protein interactions (PPI) are available which give researchers in bioinformatics the opportunity to explore and understand biological properties and structure from a networking perspective. Identification of protein complexes is a challenge that has emerged as an attraction to researchers particularly in computational biology. Various computational approaches were developed to identify protein complexes in PPI networks. In this paper, we give a new method based on the core-attachment approach with incorporation of gene expression data known as core-attachment with gene (CAG) expression to identify protein complexes in PPI networks. Experiment results support that our method CAG can detect protein complexes effectively. Validation by biological information, namely co-localisation and gene ontology semantic similarity score reveals that the complexes predicted by our method has high biological relevance. We also give a comparison of our method with four other popular methods in the field.Keywords: gene expression analysis; protein clustering; protein complexes; protein-protein interaction networksCitation: International Journal of Bioinformatics Research and Applications, Vol. 13, No. 4 (2017) pp. 313 - 328PubDate: 2017-10-14T23:20:50-05:00DOI: 10.1504/IJBRA.2017.087382Issue No:Vol. 13, No. 4 (2017)

Authors:Sanchita Gupta, Garima Singh, Swati Srivastava, Ashok SharmaPages: 329 - 351Abstract: Understanding the responses of plant against any environmental condition requires the expression analysis of transcriptome data. The present work focused on identifying the group of genes of Solanum tuberosum, differentially expressed in different abiotic stresses. The public database has assessed for the gene expression data in response to cold, heat and salt stresses, respectively. Furthermore, the common genes considered as marker genes, responding to all three abiotic conditions were analysed. The gene ontology classification of the marker genes and their visualisation in metabolic pathway was also analysed. The genes responsible for kunitz-type protease inhibitor precursor were found to be up-regulated, whereas the genes encoding lipid transfer protein showed down-regulation. These marker genes may be studied for further validation to see their role in stress responses to the medicinally important plants of solanaceae family.Keywords: abiotic stress; co-expression; functional annotation; gene expression; gene ontology; metabolic pathway; microarray analysis; network analysis; solanaceae; Solanum tuberosumCitation: International Journal of Bioinformatics Research and Applications, Vol. 13, No. 4 (2017) pp. 329 - 351PubDate: 2017-10-14T23:20:50-05:00DOI: 10.1504/IJBRA.2017.087383Issue No:Vol. 13, No. 4 (2017)

Authors:Jing Lu, Cuiqing Wang, Malcolm KeechPages: 352 - 375Abstract: Extraction of motifs from biological sequences is among the frontier research issues in bioinformatics, with sequential patterns mining becoming one of the most important computational techniques in this area. A number of applications motivate the search for more structured patterns and concurrent protein motif mining is considered here. This paper builds on the concept of structural relation patterns and applies the concurrent sequential patterns (ConSP) mining approach to biological databases. Specifically, an original method is presented using support vectors as the data structure for the extraction of novel patterns in protein sequences. Data modelling is pursued to represent the more interesting concurrent patterns visually. Experiments with real-world protein datasets from the UniProt and NCBI databases highlight the applicability of the ConSP methodology in protein data mining and modelling. The results show the potential for knowledge discovery in the field of protein structure identification. A pilot experiment extends the methodology to DNA sequences to indicate a future direction.Keywords: bioinformatics; biological databases; concurrent vector method; data analytics; DNA sequences; graphical modelling; knowledge discovery; protein motif mining; sequential patterns post-processing; structural relationsCitation: International Journal of Bioinformatics Research and Applications, Vol. 13, No. 4 (2017) pp. 352 - 375PubDate: 2017-10-14T23:20:50-05:00DOI: 10.1504/IJBRA.2017.087384Issue No:Vol. 13, No. 4 (2017)

Authors:Yulanda Antonius, Didik Huswo Utomo, WidodoPages: 376 - 388Abstract: Nasopharyngeal carcinoma (NPC) is malignant tumour that strongly related to Epstein-Barr virus infection. Several methods are available for diagnosis but it only indicates the viral titre. This research aims to identify new potential biomarker and those contributions in NPC signalling pathway. Biomarker was identified by topological analysis, modularity analysis and functional analysis using Cytoscape 3.2.1. Furthermore, biomarkers' candidate expression was confirmed by microarray data from NCBI and analyzed by non-paired t-test. The results showed four potential biomarkers with the highest value in each parameter of topological analysis such as RPA1, USP7, UBC and TERF2, but only RPA1 included in protein module with the highest score of 4.526, while UBC and TERF2 involved in protein module with lower score. Moreover, RPA1 has high expression in NPC samples (p < 0.05; FC = 1.07) and mainly related to cell cycle pathway. This study might help to understand the NPC mechanism and develop an appropriate treatment.Keywords: biomarker; latent protein; nasopharyngeal carcinoma; protein analysis; protein network; tumourigenesisCitation: International Journal of Bioinformatics Research and Applications, Vol. 13, No. 4 (2017) pp. 376 - 388PubDate: 2017-10-14T23:20:50-05:00DOI: 10.1504/IJBRA.2017.087385Issue No:Vol. 13, No. 4 (2017)